code: def _DataToInputs(
      self,
      spec,
      labels,
      weighted_labels,
      length,
      filename,
      truncated_length,
  ):
      del weighted_labels
      # This method re-implements a portion of the TensorFlow graph using numpy.
      # While typically it is frowned upon to test complicated code with other
      # code, there is no way around this for testing the pipeline end to end,
      # which requires an actual spec computation. Furthermore, much of the
      # complexity of the pipeline is due to the TensorFlow implementation,
      # so comparing it against simpler numpy code still provides effective
      # coverage.
      truncated_length = (
          min(truncated_length, length) if truncated_length else length
      )
  
      # Pad or slice spec if differs from truncated_length.
      if len(spec) < truncated_length:
          pad_amt = truncated_length - len(spec)
          spec = np.pad(spec, [(0, pad_amt), (0, 0)], "constant")
      else:
          spec = spec[0:truncated_length]
  
      # Pad or slice labels if differs from truncated_length.
      if len(labels) < truncated_length:
          pad_amt = truncated_length - len(labels)
          labels = np.pad(labels, [(0, pad_amt), (0, 0)], "constant")
      else:
          labels = labels[0:truncated_length]
  
      inputs = [(spec, labels, truncated_length, filename)]
  
      return inputs
  
imports: import copy
  import tempfile
  import time
  from magenta.models.onsets_frames_transcription import configs
  from magenta.models.onsets_frames_transcription import constants
  from magenta.models.onsets_frames_transcription import data
  from note_seq import audio_io
  from note_seq import sequences_lib
  from note_seq import testing_lib
  from note_seq.protobuf import music_pb2
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the DataTest obj's underlying member method code with example usage: obj._DataToInputs(spec, labels, weighted_labels, length, filename, truncated_length) and returns: inputs